Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations102
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory108.6 KiB
Average record size in memory1.1 KiB

Variable types

Numeric20
Text2
Categorical1

Alerts

Engagement_Rate is highly overall correlated with Home_ratio and 1 other fieldsHigh correlation
Explore_ratio is highly overall correlated with From Explore and 2 other fieldsHigh correlation
Follows is highly overall correlated with From Explore and 7 other fieldsHigh correlation
From Explore is highly overall correlated with Explore_ratio and 6 other fieldsHigh correlation
From Hashtags is highly overall correlated with Follows and 6 other fieldsHigh correlation
From Home is highly overall correlated with Impressions and 4 other fieldsHigh correlation
From Other is highly overall correlated with Follows and 2 other fieldsHigh correlation
Hashtag_ratio is highly overall correlated with From Hashtags and 2 other fieldsHigh correlation
Home_ratio is highly overall correlated with Engagement_Rate and 8 other fieldsHigh correlation
Impressions is highly overall correlated with Follows and 8 other fieldsHigh correlation
Likes is highly overall correlated with Follows and 8 other fieldsHigh correlation
Other_ratio is highly overall correlated with From OtherHigh correlation
Post_ID is highly overall correlated with Follows and 2 other fieldsHigh correlation
Profile Visits is highly overall correlated with Follows and 4 other fieldsHigh correlation
Saves is highly overall correlated with From Explore and 5 other fieldsHigh correlation
Shares is highly overall correlated with From Home and 3 other fieldsHigh correlation
Top_source is highly overall correlated with Explore_ratio and 6 other fieldsHigh correlation
saves_to_likes is highly overall correlated with Engagement_Rate and 6 other fieldsHigh correlation
Post_ID is uniformly distributed Uniform
Post_ID has unique values Unique
Engagement_Rate has unique values Unique
Home_ratio has unique values Unique
Explore_ratio has unique values Unique
Hashtag_ratio has unique values Unique
Other_ratio has unique values Unique
saves_to_likes has unique values Unique
Comments has 3 (2.9%) zeros Zeros
Shares has 5 (4.9%) zeros Zeros
Follows has 8 (7.8%) zeros Zeros

Reproduction

Analysis started2025-09-13 11:15:38.733873
Analysis finished2025-09-13 11:16:35.818444
Duration57.08 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Post_ID
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct102
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.5
Minimum1
Maximum102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-09-13T14:16:35.953116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.05
Q126.25
median51.5
Q376.75
95-th percentile96.95
Maximum102
Range101
Interquartile range (IQR)50.5

Descriptive statistics

Standard deviation29.588849
Coefficient of variation (CV)0.57454076
Kurtosis-1.2
Mean51.5
Median Absolute Deviation (MAD)25.5
Skewness0
Sum5253
Variance875.5
MonotonicityStrictly increasing
2025-09-13T14:16:36.193144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
2 1
 
1.0%
3 1
 
1.0%
4 1
 
1.0%
5 1
 
1.0%
6 1
 
1.0%
7 1
 
1.0%
8 1
 
1.0%
9 1
 
1.0%
10 1
 
1.0%
Other values (92) 92
90.2%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
102 1
1.0%
101 1
1.0%
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%

Impressions
Real number (ℝ)

High correlation 

Distinct101
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5920.2549
Minimum1941
Maximum36919
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-09-13T14:16:36.407643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1941
5-th percentile2412.55
Q13556
median4343.5
Q36296.25
95-th percentile13572.45
Maximum36919
Range34978
Interquartile range (IQR)2740.25

Descriptive statistics

Standard deviation5139.8881
Coefficient of variation (CV)0.86818696
Kurtosis19.417842
Mean5920.2549
Median Absolute Deviation (MAD)1103.5
Skewness3.9777248
Sum603866
Variance26418450
MonotonicityNot monotonic
2025-09-13T14:16:36.599370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5394 2
 
2.0%
3920 1
 
1.0%
4021 1
 
1.0%
4528 1
 
1.0%
2518 1
 
1.0%
3884 1
 
1.0%
2621 1
 
1.0%
3541 1
 
1.0%
3749 1
 
1.0%
4115 1
 
1.0%
Other values (91) 91
89.2%
ValueCountFrequency (%)
1941 1
1.0%
2064 1
1.0%
2191 1
1.0%
2218 1
1.0%
2327 1
1.0%
2407 1
1.0%
2518 1
1.0%
2523 1
1.0%
2621 1
1.0%
2766 1
1.0%
ValueCountFrequency (%)
36919 1
1.0%
32695 1
1.0%
17713 1
1.0%
17396 1
1.0%
16062 1
1.0%
13700 1
1.0%
11149 1
1.0%
11068 1
1.0%
10933 1
1.0%
10667 1
1.0%

From Home
Real number (ℝ)

High correlation 

Distinct97
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2496.9118
Minimum1133
Maximum13473
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-09-13T14:16:36.798942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1133
5-th percentile1344.4
Q11923.75
median2216
Q32605.25
95-th percentile3808.2
Maximum13473
Range12340
Interquartile range (IQR)681.5

Descriptive statistics

Standard deviation1588.3774
Coefficient of variation (CV)0.63613677
Kurtosis33.484056
Mean2496.9118
Median Absolute Deviation (MAD)365.5
Skewness5.4045063
Sum254685
Variance2522942.8
MonotonicityNot monotonic
2025-09-13T14:16:37.059590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1975 3
 
2.9%
2415 2
 
2.0%
3152 2
 
2.0%
2125 2
 
2.0%
2586 1
 
1.0%
2046 1
 
1.0%
1543 1
 
1.0%
2727 1
 
1.0%
2085 1
 
1.0%
2609 1
 
1.0%
Other values (87) 87
85.3%
ValueCountFrequency (%)
1133 1
1.0%
1179 1
1.0%
1304 1
1.0%
1308 1
1.0%
1323 1
1.0%
1338 1
1.0%
1466 1
1.0%
1502 1
1.0%
1543 1
1.0%
1570 1
1.0%
ValueCountFrequency (%)
13473 1
1.0%
11815 1
1.0%
5185 1
1.0%
4439 1
1.0%
4137 1
1.0%
3813 1
1.0%
3717 1
1.0%
3401 1
1.0%
3152 2
2.0%
3144 1
1.0%

From Hashtags
Real number (ℝ)

High correlation 

Distinct100
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1968.2843
Minimum116
Maximum11817
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-09-13T14:16:37.349798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum116
5-th percentile214.15
Q1753
median1326
Q32415.75
95-th percentile5761.8
Maximum11817
Range11701
Interquartile range (IQR)1662.75

Descriptive statistics

Standard deviation1977.2981
Coefficient of variation (CV)1.0045795
Kurtosis8.1060627
Mean1968.2843
Median Absolute Deviation (MAD)718.5
Skewness2.504723
Sum200765
Variance3909707.9
MonotonicityNot monotonic
2025-09-13T14:16:37.633472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
362 2
 
2.0%
411 2
 
2.0%
1028 1
 
1.0%
1838 1
 
1.0%
621 1
 
1.0%
1188 1
 
1.0%
599 1
 
1.0%
255 1
 
1.0%
628 1
 
1.0%
857 1
 
1.0%
Other values (90) 90
88.2%
ValueCountFrequency (%)
116 1
1.0%
139 1
1.0%
166 1
1.0%
183 1
1.0%
201 1
1.0%
212 1
1.0%
255 1
1.0%
278 1
1.0%
349 1
1.0%
362 2
2.0%
ValueCountFrequency (%)
11817 1
1.0%
10008 1
1.0%
7761 1
1.0%
6610 1
1.0%
6564 1
1.0%
5799 1
1.0%
5055 1
1.0%
4604 1
1.0%
4221 1
1.0%
4176 1
1.0%

From Explore
Real number (ℝ)

High correlation 

Distinct95
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1178.5686
Minimum0
Maximum17414
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-09-13T14:16:37.854923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile45.15
Q1178.75
median337
Q3728.5
95-th percentile5619.9
Maximum17414
Range17414
Interquartile range (IQR)549.75

Descriptive statistics

Standard deviation2797.2126
Coefficient of variation (CV)2.3733982
Kurtosis21.208058
Mean1178.5686
Median Absolute Deviation (MAD)223.5
Skewness4.4403695
Sum120214
Variance7824398.4
MonotonicityNot monotonic
2025-09-13T14:16:38.018870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
248 2
 
2.0%
45 2
 
2.0%
48 2
 
2.0%
84 2
 
2.0%
360 2
 
2.0%
182 2
 
2.0%
468 2
 
2.0%
333 1
 
1.0%
500 1
 
1.0%
178 1
 
1.0%
Other values (85) 85
83.3%
ValueCountFrequency (%)
0 1
1.0%
29 1
1.0%
36 1
1.0%
37 1
1.0%
45 2
2.0%
48 2
2.0%
51 1
1.0%
59 1
1.0%
60 1
1.0%
69 1
1.0%
ValueCountFrequency (%)
17414 1
1.0%
16444 1
1.0%
12389 1
1.0%
6000 1
1.0%
5762 1
1.0%
5634 1
1.0%
5352 1
1.0%
5192 1
1.0%
2355 1
1.0%
2266 1
1.0%

From Other
Real number (ℝ)

High correlation 

Distinct84
Distinct (%)82.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean184.54902
Minimum9
Maximum2547
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-09-13T14:16:38.275295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile21.1
Q140.25
median75
Q3218.5
95-th percentile650.3
Maximum2547
Range2538
Interquartile range (IQR)178.25

Descriptive statistics

Standard deviation309.09605
Coefficient of variation (CV)1.6748724
Kurtosis34.227684
Mean184.54902
Median Absolute Deviation (MAD)47.5
Skewness5.0572365
Sum18824
Variance95540.369
MonotonicityNot monotonic
2025-09-13T14:16:38.534727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72 3
 
2.9%
73 2
 
2.0%
43 2
 
2.0%
25 2
 
2.0%
75 2
 
2.0%
18 2
 
2.0%
32 2
 
2.0%
60 2
 
2.0%
26 2
 
2.0%
34 2
 
2.0%
Other values (74) 81
79.4%
ValueCountFrequency (%)
9 1
1.0%
15 1
1.0%
17 1
1.0%
18 2
2.0%
21 1
1.0%
23 1
1.0%
24 1
1.0%
25 2
2.0%
26 2
2.0%
27 2
2.0%
ValueCountFrequency (%)
2547 1
1.0%
1115 1
1.0%
794 1
1.0%
792 1
1.0%
748 1
1.0%
655 1
1.0%
561 1
1.0%
536 1
1.0%
533 1
1.0%
532 1
1.0%

Saves
Real number (ℝ)

High correlation 

Distinct84
Distinct (%)82.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156.54902
Minimum22
Maximum1095
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-09-13T14:16:38.783415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile35.05
Q170.5
median111
Q3173.5
95-th percentile466.6
Maximum1095
Range1073
Interquartile range (IQR)103

Descriptive statistics

Standard deviation157.77033
Coefficient of variation (CV)1.0078015
Kurtosis13.44031
Mean156.54902
Median Absolute Deviation (MAD)56
Skewness3.1729853
Sum15968
Variance24891.478
MonotonicityNot monotonic
2025-09-13T14:16:38.987623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135 3
 
2.9%
144 3
 
2.9%
74 2
 
2.0%
98 2
 
2.0%
49 2
 
2.0%
40 2
 
2.0%
111 2
 
2.0%
99 2
 
2.0%
90 2
 
2.0%
34 2
 
2.0%
Other values (74) 80
78.4%
ValueCountFrequency (%)
22 1
1.0%
28 1
1.0%
33 1
1.0%
34 2
2.0%
35 1
1.0%
36 1
1.0%
38 2
2.0%
40 2
2.0%
41 1
1.0%
42 2
2.0%
ValueCountFrequency (%)
1095 1
1.0%
668 1
1.0%
653 1
1.0%
573 1
1.0%
504 1
1.0%
469 1
1.0%
421 1
1.0%
393 1
1.0%
342 1
1.0%
318 1
1.0%

Comments
Real number (ℝ)

Zeros 

Distinct15
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3529412
Minimum0
Maximum19
Zeros3
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-09-13T14:16:39.118078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.05
Q14
median6
Q38
95-th percentile11
Maximum19
Range19
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.3080971
Coefficient of variation (CV)0.52071898
Kurtosis1.8884371
Mean6.3529412
Median Absolute Deviation (MAD)2
Skewness0.76190046
Sum648
Variance10.943506
MonotonicityNot monotonic
2025-09-13T14:16:39.207538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
6 16
15.7%
4 12
11.8%
7 12
11.8%
8 12
11.8%
5 10
9.8%
9 8
7.8%
3 7
6.9%
11 6
 
5.9%
2 5
 
4.9%
10 4
 
3.9%
Other values (5) 10
9.8%
ValueCountFrequency (%)
0 3
 
2.9%
1 3
 
2.9%
2 5
 
4.9%
3 7
6.9%
4 12
11.8%
5 10
9.8%
6 16
15.7%
7 12
11.8%
8 12
11.8%
9 8
7.8%
ValueCountFrequency (%)
19 1
 
1.0%
17 1
 
1.0%
13 2
 
2.0%
11 6
 
5.9%
10 4
 
3.9%
9 8
7.8%
8 12
11.8%
7 12
11.8%
6 16
15.7%
5 10
9.8%

Shares
Real number (ℝ)

High correlation  Zeros 

Distinct28
Distinct (%)27.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.3039216
Minimum0
Maximum75
Zeros5
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-09-13T14:16:39.319315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6.5
Q313
95-th percentile22.95
Maximum75
Range75
Interquartile range (IQR)10

Descriptive statistics

Standard deviation10.150149
Coefficient of variation (CV)1.0909539
Kurtosis17.459759
Mean9.3039216
Median Absolute Deviation (MAD)4.5
Skewness3.3448765
Sum949
Variance103.02553
MonotonicityNot monotonic
2025-09-13T14:16:39.450695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
3 11
 
10.8%
1 10
 
9.8%
4 7
 
6.9%
5 7
 
6.9%
6 6
 
5.9%
15 6
 
5.9%
8 5
 
4.9%
7 5
 
4.9%
0 5
 
4.9%
2 5
 
4.9%
Other values (18) 35
34.3%
ValueCountFrequency (%)
0 5
4.9%
1 10
9.8%
2 5
4.9%
3 11
10.8%
4 7
6.9%
5 7
6.9%
6 6
5.9%
7 5
4.9%
8 5
4.9%
9 3
 
2.9%
ValueCountFrequency (%)
75 1
1.0%
41 1
1.0%
38 1
1.0%
27 1
1.0%
26 1
1.0%
23 1
1.0%
22 2
2.0%
20 2
2.0%
19 1
1.0%
18 1
1.0%

Likes
Real number (ℝ)

High correlation 

Distinct85
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean176.82353
Minimum72
Maximum549
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-09-13T14:16:39.611646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum72
5-th percentile82.15
Q1122
median157.5
Q3208.75
95-th percentile327
Maximum549
Range477
Interquartile range (IQR)86.75

Descriptive statistics

Standard deviation85.151747
Coefficient of variation (CV)0.48156344
Kurtosis4.0043524
Mean176.82353
Median Absolute Deviation (MAD)41
Skewness1.7191257
Sum18036
Variance7250.82
MonotonicityNot monotonic
2025-09-13T14:16:39.737903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
151 3
 
2.9%
114 3
 
2.9%
159 2
 
2.0%
76 2
 
2.0%
142 2
 
2.0%
72 2
 
2.0%
96 2
 
2.0%
86 2
 
2.0%
416 2
 
2.0%
150 2
 
2.0%
Other values (75) 80
78.4%
ValueCountFrequency (%)
72 2
2.0%
76 2
2.0%
81 1
1.0%
82 1
1.0%
85 1
1.0%
86 2
2.0%
91 1
1.0%
92 2
2.0%
94 1
1.0%
95 1
1.0%
ValueCountFrequency (%)
549 1
1.0%
443 1
1.0%
416 2
2.0%
373 1
1.0%
328 1
1.0%
308 1
1.0%
301 1
1.0%
297 1
1.0%
294 1
1.0%
275 1
1.0%

Profile Visits
Real number (ℝ)

High correlation 

Distinct59
Distinct (%)57.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.666667
Minimum4
Maximum611
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-09-13T14:16:39.860971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile8.05
Q116
median24
Q345.75
95-th percentile234.2
Maximum611
Range607
Interquartile range (IQR)29.75

Descriptive statistics

Standard deviation93.169954
Coefficient of variation (CV)1.7043284
Kurtosis16.916099
Mean54.666667
Median Absolute Deviation (MAD)12
Skewness3.8859065
Sum5576
Variance8680.6403
MonotonicityNot monotonic
2025-09-13T14:16:39.998501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26 5
 
4.9%
21 5
 
4.9%
8 4
 
3.9%
20 4
 
3.9%
19 4
 
3.9%
22 4
 
3.9%
12 3
 
2.9%
16 3
 
2.9%
15 3
 
2.9%
14 3
 
2.9%
Other values (49) 64
62.7%
ValueCountFrequency (%)
4 1
 
1.0%
7 1
 
1.0%
8 4
3.9%
9 3
2.9%
10 2
2.0%
11 3
2.9%
12 3
2.9%
13 2
2.0%
14 3
2.9%
15 3
2.9%
ValueCountFrequency (%)
611 1
1.0%
467 1
1.0%
347 1
1.0%
330 1
1.0%
306 1
1.0%
237 1
1.0%
181 1
1.0%
155 1
1.0%
148 1
1.0%
144 1
1.0%

Follows
Real number (ℝ)

High correlation  Zeros 

Distinct29
Distinct (%)28.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.823529
Minimum0
Maximum260
Zeros8
Zeros (%)7.8%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-09-13T14:16:40.790823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median8
Q318
95-th percentile95.9
Maximum260
Range260
Interquartile range (IQR)14

Descriptive statistics

Standard deviation43.685966
Coefficient of variation (CV)1.9140758
Kurtosis15.51751
Mean22.823529
Median Absolute Deviation (MAD)6
Skewness3.7532633
Sum2328
Variance1908.4636
MonotonicityNot monotonic
2025-09-13T14:16:40.906473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
4 13
12.7%
6 12
11.8%
2 12
11.8%
10 9
 
8.8%
8 8
 
7.8%
0 8
 
7.8%
12 7
 
6.9%
18 4
 
3.9%
16 3
 
2.9%
26 2
 
2.0%
Other values (19) 24
23.5%
ValueCountFrequency (%)
0 8
7.8%
2 12
11.8%
4 13
12.7%
6 12
11.8%
8 8
7.8%
10 9
8.8%
12 7
6.9%
14 2
 
2.0%
16 3
 
2.9%
18 4
 
3.9%
ValueCountFrequency (%)
260 1
1.0%
228 1
1.0%
214 1
1.0%
100 2
2.0%
96 1
1.0%
94 2
2.0%
80 1
1.0%
74 1
1.0%
58 1
1.0%
46 1
1.0%
Distinct90
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Memory size26.3 KiB
2025-09-13T14:16:41.296102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length784
Median length216.5
Mean length179.68627
Min length44

Characters and Unicode

Total characters18328
Distinct characters73
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique79 ?
Unique (%)77.5%

Sample

1st rowHere are some of the most important data visualizations that every Financial Data Analyst/Scientist should know.
2nd rowHere are some of the best data science project ideas on healthcare. If you want to become a data science professional in the healthcare domain then you must try to work on these projects.
3rd rowLearn how to train a machine learning model and giving inputs to your trained model to make predictions using Python.
4th rowHere’s how you can write a Python program to detect whether a sentence is a question or not. The idea here is to find the words that we see in the beginning of a question in the beginning of a sentence.
5th rowPlotting annotations while visualizing your data is considered good practice to make the graphs self-explanatory. Here is an example of how you can annotate a graph using Python.
ValueCountFrequency (%)
the 163
 
5.2%
of 125
 
4.0%
to 113
 
3.6%
data 95
 
3.0%
you 94
 
3.0%
a 81
 
2.6%
here 74
 
2.3%
are 67
 
2.1%
in 60
 
1.9%
python 50
 
1.6%
Other values (549) 2228
70.7%
2025-09-13T14:16:41.864027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3047
16.6%
e 1864
 
10.2%
t 1320
 
7.2%
a 1288
 
7.0%
o 1212
 
6.6%
n 1125
 
6.1%
i 1017
 
5.5%
s 960
 
5.2%
r 925
 
5.0%
h 590
 
3.2%
Other values (63) 4980
27.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18328
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3047
16.6%
e 1864
 
10.2%
t 1320
 
7.2%
a 1288
 
7.0%
o 1212
 
6.6%
n 1125
 
6.1%
i 1017
 
5.5%
s 960
 
5.2%
r 925
 
5.0%
h 590
 
3.2%
Other values (63) 4980
27.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18328
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3047
16.6%
e 1864
 
10.2%
t 1320
 
7.2%
a 1288
 
7.0%
o 1212
 
6.6%
n 1125
 
6.1%
i 1017
 
5.5%
s 960
 
5.2%
r 925
 
5.0%
h 590
 
3.2%
Other values (63) 4980
27.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18328
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3047
16.6%
e 1864
 
10.2%
t 1320
 
7.2%
a 1288
 
7.0%
o 1212
 
6.6%
n 1125
 
6.1%
i 1017
 
5.5%
s 960
 
5.2%
r 925
 
5.0%
h 590
 
3.2%
Other values (63) 4980
27.2%
Distinct54
Distinct (%)52.9%
Missing0
Missing (%)0.0%
Memory size61.2 KiB
2025-09-13T14:16:42.080442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length406
Median length323
Mean length261.23529
Min length153

Characters and Unicode

Total characters26646
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)34.3%

Sample

1st row#finance #money #business #investing #investment #trading #stockmarket #data #datascience #dataanalysis #dataanalytics #datascientist #machinelearning #python #pythonprogramming #pythonprojects #pythoncode #artificialintelligence #ai #dataanalyst #amankharwal #thecleverprogrammer
2nd row#healthcare #health #covid #data #datascience #dataanalysis #dataanalytics #datascientist #machinelearning #python #pythonprogramming #pythonprojects #pythoncode #artificialintelligence #ai #dataanalyst #amankharwal #thecleverprogrammer
3rd row#data #datascience #dataanalysis #dataanalytics #datascientist #machinelearning #python #pythonprogramming #pythonprojects #pythoncode #artificialintelligence #ai #deeplearning #machinelearningprojects #datascienceprojects #amankharwal #thecleverprogrammer #machinelearningmodels
4th row#python #pythonprogramming #pythonprojects #pythoncode #pythonlearning #pythondeveloper #pythoncoding #pythonprogrammer #amankharwal #thecleverprogrammer #pythonprojects
5th row#datavisualization #datascience #data #dataanalytics #machinelearning #dataanalysis #artificialintelligence #python #datascientist #bigdata #deeplearning #dataviz #ai #analytics #technology #dataanalyst #programming #pythonprogramming #statistics #coding #businessintelligence #datamining #tech #business #computerscience #tableau #database #thecleverprogrammer #amankharwal
ValueCountFrequency (%)
amankharwal 100
 
5.3%
thecleverprogrammer 100
 
5.3%
python 93
 
4.9%
pythonprogramming 84
 
4.4%
pythonprojects 82
 
4.3%
machinelearning 81
 
4.3%
datascience 79
 
4.2%
ai 77
 
4.1%
artificialintelligence 75
 
4.0%
data 74
 
3.9%
Other values (154) 1048
55.4%
2025-09-13T14:16:42.497906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2827
 
10.6%
e 2282
 
8.6%
n 2092
 
7.9%
t 1895
 
7.1%
# 1892
 
7.1%
  1791
 
6.7%
i 1787
 
6.7%
r 1505
 
5.6%
c 1320
 
5.0%
o 1224
 
4.6%
Other values (22) 8031
30.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26646
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2827
 
10.6%
e 2282
 
8.6%
n 2092
 
7.9%
t 1895
 
7.1%
# 1892
 
7.1%
  1791
 
6.7%
i 1787
 
6.7%
r 1505
 
5.6%
c 1320
 
5.0%
o 1224
 
4.6%
Other values (22) 8031
30.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26646
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2827
 
10.6%
e 2282
 
8.6%
n 2092
 
7.9%
t 1895
 
7.1%
# 1892
 
7.1%
  1791
 
6.7%
i 1787
 
6.7%
r 1505
 
5.6%
c 1320
 
5.0%
o 1224
 
4.6%
Other values (22) 8031
30.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26646
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2827
 
10.6%
e 2282
 
8.6%
n 2092
 
7.9%
t 1895
 
7.1%
# 1892
 
7.1%
  1791
 
6.7%
i 1787
 
6.7%
r 1505
 
5.6%
c 1320
 
5.0%
o 1224
 
4.6%
Other values (22) 8031
30.1%

Engagement_Rate
Real number (ℝ)

High correlation  Unique 

Distinct102
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3185371
Minimum3.0526287
Maximum13.033833
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-09-13T14:16:42.608404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3.0526287
5-th percentile3.3722643
Q14.7709739
median6.2208744
Q37.4704614
95-th percentile9.3570138
Maximum13.033833
Range9.9812038
Interquartile range (IQR)2.6994875

Descriptive statistics

Standard deviation2.0474364
Coefficient of variation (CV)0.32403646
Kurtosis0.53568084
Mean6.3185371
Median Absolute Deviation (MAD)1.395258
Skewness0.69763743
Sum644.49079
Variance4.1919959
MonotonicityNot monotonic
2025-09-13T14:16:42.767730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.989795918 1
 
1.0%
8.138672599 1
 
1.0%
4.575976125 1
 
1.0%
8.878091873 1
 
1.0%
9.054805401 1
 
1.0%
6.05046344 1
 
1.0%
3.967951164 1
 
1.0%
7.681445919 1
 
1.0%
8.748999733 1
 
1.0%
7.825030377 1
 
1.0%
Other values (92) 92
90.2%
ValueCountFrequency (%)
3.052628728 1
1.0%
3.116694854 1
1.0%
3.118546055 1
1.0%
3.265968964 1
1.0%
3.351735016 1
1.0%
3.364147527 1
1.0%
3.526483197 1
1.0%
3.643389456 1
1.0%
3.733098178 1
1.0%
3.762029746 1
1.0%
ValueCountFrequency (%)
13.0338325 1
1.0%
11.93578409 1
1.0%
11.35975725 1
1.0%
10.97739947 1
1.0%
10.06162141 1
1.0%
9.36468102 1
1.0%
9.21133703 1
1.0%
9.206877427 1
1.0%
9.12183055 1
1.0%
9.054805401 1
1.0%

Home_ratio
Real number (ℝ)

High correlation  Unique 

Distinct102
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9718113
Minimum1.044493
Maximum9.186551
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-09-13T14:16:42.895462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.044493
5-th percentile2.3352533
Q13.7562499
median4.8979119
Q36.2270015
95-th percentile7.6200125
Maximum9.186551
Range8.142058
Interquartile range (IQR)2.4707516

Descriptive statistics

Standard deviation1.6746244
Coefficient of variation (CV)0.3368238
Kurtosis-0.3703139
Mean4.9718113
Median Absolute Deviation (MAD)1.2089715
Skewness0.082322193
Sum507.12476
Variance2.8043668
MonotonicityNot monotonic
2025-09-13T14:16:43.029568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.596938776 1
 
1.0%
5.055617353 1
 
1.0%
5.185277294 1
 
1.0%
5.962897527 1
 
1.0%
6.767275616 1
 
1.0%
5.267765191 1
 
1.0%
5.887066005 1
 
1.0%
5.84863033 1
 
1.0%
6.359029074 1
 
1.0%
6.340218712 1
 
1.0%
Other values (92) 92
90.2%
ValueCountFrequency (%)
1.044492987 1
1.0%
1.38260035 1
1.0%
1.896458258 1
1.0%
1.957415017 1
1.0%
2.007052321 1
1.0%
2.323143249 1
1.0%
2.565343659 1
1.0%
2.671109701 1
1.0%
2.737376178 1
1.0%
2.825896763 1
1.0%
ValueCountFrequency (%)
9.186550976 1
1.0%
8.545216252 1
1.0%
8.498250875 1
1.0%
7.848258706 1
1.0%
7.726432532 1
1.0%
7.623549635 1
1.0%
7.552807831 1
1.0%
7.464440322 1
1.0%
7.45930644 1
1.0%
7.200180343 1
1.0%

Explore_ratio
Real number (ℝ)

High correlation  Unique 

Distinct102
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2916152
Minimum0
Maximum6.994298
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-09-13T14:16:43.260777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.11599745
Q10.40262753
median0.75650866
Q31.5149551
95-th percentile4.4416151
Maximum6.994298
Range6.994298
Interquartile range (IQR)1.1123276

Descriptive statistics

Standard deviation1.3936482
Coefficient of variation (CV)1.0789964
Kurtosis4.041122
Mean1.2916152
Median Absolute Deviation (MAD)0.47997165
Skewness2.0200954
Sum131.74475
Variance1.9422553
MonotonicityNot monotonic
2025-09-13T14:16:43.455241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.579081633 1
 
1.0%
2.176492399 1
 
1.0%
0 1
 
1.0%
2.058303887 1
 
1.0%
1.10802224 1
 
1.0%
0.8470648816 1
 
1.0%
1.27050744 1
 
1.0%
1.4120305 1
 
1.0%
0.6615097359 1
 
1.0%
0.432563791 1
 
1.0%
Other values (92) 92
90.2%
ValueCountFrequency (%)
0 1
1.0%
0.07200720072 1
1.0%
0.07572172267 1
1.0%
0.08342602892 1
1.0%
0.08492569002 1
1.0%
0.1149425287 1
1.0%
0.1360410095 1
1.0%
0.1556824079 1
1.0%
0.1843817787 1
1.0%
0.19062339 1
1.0%
ValueCountFrequency (%)
6.994297973 1
1.0%
5.878894768 1
1.0%
5.326196666 1
1.0%
5.168176518 1
1.0%
5.09035056 1
1.0%
4.454075137 1
1.0%
4.204874019 1
1.0%
3.953934741 1
1.0%
3.906569343 1
1.0%
3.302730128 1
1.0%

Hashtag_ratio
Real number (ℝ)

High correlation  Unique 

Distinct102
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2260669
Minimum0.31917336
Maximum7.3963595
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-09-13T14:16:43.702802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.31917336
5-th percentile0.65861551
Q11.8708091
median2.9796057
Q34.3664069
95-th percentile6.160628
Maximum7.3963595
Range7.0771861
Interquartile range (IQR)2.4955978

Descriptive statistics

Standard deviation1.7726343
Coefficient of variation (CV)0.54947226
Kurtosis-0.61951141
Mean3.2260669
Median Absolute Deviation (MAD)1.2355117
Skewness0.40011406
Sum329.05882
Variance3.1422322
MonotonicityNot monotonic
2025-09-13T14:16:43.854777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.62244898 1
 
1.0%
3.407489803 1
 
1.0%
2.954488933 1
 
1.0%
1.371466431 1
 
1.0%
1.012708499 1
 
1.0%
3.125643666 1
 
1.0%
2.285387257 1
 
1.0%
1.773510308 1
 
1.0%
2.285942918 1
 
1.0%
2.682867558 1
 
1.0%
Other values (92) 92
90.2%
ValueCountFrequency (%)
0.3191733639 1
1.0%
0.4193781634 1
1.0%
0.5074875208 1
1.0%
0.5113986445 1
1.0%
0.6507280244 1
1.0%
0.6585845347 1
1.0%
0.6592039801 1
1.0%
0.670015248 1
1.0%
0.6946526737 1
1.0%
0.9625324973 1
1.0%
ValueCountFrequency (%)
7.396359478 1
1.0%
7.357116175 1
1.0%
6.906690669 1
1.0%
6.366934848 1
1.0%
6.317960255 1
1.0%
6.161000152 1
1.0%
6.153557701 1
1.0%
6.134560457 1
1.0%
6.092271293 1
1.0%
6.045916034 1
1.0%

Other_ratio
Real number (ℝ)

High correlation  Unique 

Distinct102
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.31524964
Minimum0.032537961
Maximum1.9084337
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-09-13T14:16:43.984053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.032537961
5-th percentile0.047760787
Q10.11034841
median0.15280197
Q30.30540203
95-th percentile1.2432052
Maximum1.9084337
Range1.8758958
Interquartile range (IQR)0.19505362

Descriptive statistics

Standard deviation0.38489722
Coefficient of variation (CV)1.2209284
Kurtosis5.3611726
Mean0.31524964
Median Absolute Deviation (MAD)0.068110042
Skewness2.338037
Sum32.155463
Variance0.14814587
MonotonicityNot monotonic
2025-09-13T14:16:44.119967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1428571429 1
 
1.0%
0.1446051168 1
 
1.0%
1.32554091 1
 
1.0%
0.1612190813 1
 
1.0%
0.1469420175 1
 
1.0%
0.1107106076 1
 
1.0%
0.09538344143 1
 
1.0%
0.16944366 1
 
1.0%
0.1307015204 1
 
1.0%
0.1117861482 1
 
1.0%
Other values (92) 92
90.2%
ValueCountFrequency (%)
0.03253796095 1
1.0%
0.03450345035 1
1.0%
0.04256712508 1
1.0%
0.042593469 1
1.0%
0.04530581425 1
1.0%
0.04753789578 1
1.0%
0.051995718 1
1.0%
0.05595709956 1
1.0%
0.05617977528 1
1.0%
0.05620437956 1
1.0%
ValueCountFrequency (%)
1.908433735 1
1.0%
1.80789401 1
1.0%
1.472724871 1
1.0%
1.403876375 1
1.0%
1.32554091 1
1.0%
1.247886371 1
1.0%
1.154263398 1
1.0%
1.027331411 1
1.0%
0.839944991 1
1.0%
0.8319467554 1
1.0%

saves_to_likes
Real number (ℝ)

High correlation  Unique 

Distinct102
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8209784
Minimum2.8947368
Maximum20.365854
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-09-13T14:16:44.250141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.8947368
5-th percentile3.4593892
Q15.3205882
median7.2161466
Q39.13653
95-th percentile15.330854
Maximum20.365854
Range17.471117
Interquartile range (IQR)3.8159417

Descriptive statistics

Standard deviation3.7317167
Coefficient of variation (CV)0.47714192
Kurtosis1.7388656
Mean7.8209784
Median Absolute Deviation (MAD)1.9234933
Skewness1.2758481
Sum797.7398
Variance13.925709
MonotonicityNot monotonic
2025-09-13T14:16:44.393130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.049382716 1
 
1.0%
8.660714286 1
 
1.0%
3.129770992 1
 
1.0%
8.075117371 1
 
1.0%
7.804878049 1
 
1.0%
5.138888889 1
 
1.0%
2.894736842 1
 
1.0%
10.88709677 1
 
1.0%
9.748427673 1
 
1.0%
6.387434555 1
 
1.0%
Other values (92) 92
90.2%
ValueCountFrequency (%)
2.894736842 1
1.0%
3.129770992 1
1.0%
3.333333333 1
1.0%
3.401360544 1
1.0%
3.4375 1
1.0%
3.456790123 1
1.0%
3.50877193 1
1.0%
3.551401869 1
1.0%
3.578947368 1
1.0%
3.652173913 1
1.0%
ValueCountFrequency (%)
20.36585366 1
1.0%
19.94535519 1
1.0%
18.83534137 1
1.0%
16.36363636 1
1.0%
15.65055762 1
1.0%
15.36193029 1
1.0%
14.74040632 1
1.0%
14.49152542 1
1.0%
14.29090909 1
1.0%
13 1
1.0%

Caption_Length
Real number (ℝ)

Distinct76
Distinct (%)74.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean179.68627
Minimum44
Maximum784
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-09-13T14:16:44.553850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile66.05
Q187
median131
Q3230.25
95-th percentile423.7
Maximum784
Range740
Interquartile range (IQR)143.25

Descriptive statistics

Standard deviation128.16681
Coefficient of variation (CV)0.71328105
Kurtosis4.7759639
Mean179.68627
Median Absolute Deviation (MAD)56
Skewness1.8851932
Sum18328
Variance16426.732
MonotonicityNot monotonic
2025-09-13T14:16:44.735138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79 4
 
3.9%
87 4
 
3.9%
91 3
 
2.9%
187 3
 
2.9%
81 2
 
2.0%
181 2
 
2.0%
106 2
 
2.0%
233 2
 
2.0%
104 2
 
2.0%
85 2
 
2.0%
Other values (66) 76
74.5%
ValueCountFrequency (%)
44 1
1.0%
53 1
1.0%
60 1
1.0%
63 1
1.0%
66 2
2.0%
67 1
1.0%
68 2
2.0%
69 1
1.0%
70 1
1.0%
77 1
1.0%
ValueCountFrequency (%)
784 1
1.0%
575 1
1.0%
504 1
1.0%
489 1
1.0%
447 1
1.0%
424 1
1.0%
418 1
1.0%
404 1
1.0%
367 1
1.0%
363 1
1.0%

Top_source
Categorical

High correlation 

Distinct3
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
Home_ratio
66 
Hashtag_ratio
27 
Explore_ratio

Length

Max length13
Median length10
Mean length11.058824
Min length10

Characters and Unicode

Total characters1128
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHome_ratio
2nd rowHome_ratio
3rd rowHome_ratio
4th rowHome_ratio
5th rowHome_ratio

Common Values

ValueCountFrequency (%)
Home_ratio 66
64.7%
Hashtag_ratio 27
26.5%
Explore_ratio 9
 
8.8%

Length

2025-09-13T14:16:44.880933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-13T14:16:45.044499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
home_ratio 66
64.7%
hashtag_ratio 27
26.5%
explore_ratio 9
 
8.8%

Most occurring characters

ValueCountFrequency (%)
o 177
15.7%
a 156
13.8%
t 129
11.4%
r 111
9.8%
_ 102
9.0%
i 102
9.0%
H 93
8.2%
e 75
6.6%
m 66
 
5.9%
s 27
 
2.4%
Other values (6) 90
8.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1128
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 177
15.7%
a 156
13.8%
t 129
11.4%
r 111
9.8%
_ 102
9.0%
i 102
9.0%
H 93
8.2%
e 75
6.6%
m 66
 
5.9%
s 27
 
2.4%
Other values (6) 90
8.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1128
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 177
15.7%
a 156
13.8%
t 129
11.4%
r 111
9.8%
_ 102
9.0%
i 102
9.0%
H 93
8.2%
e 75
6.6%
m 66
 
5.9%
s 27
 
2.4%
Other values (6) 90
8.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1128
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 177
15.7%
a 156
13.8%
t 129
11.4%
r 111
9.8%
_ 102
9.0%
i 102
9.0%
H 93
8.2%
e 75
6.6%
m 66
 
5.9%
s 27
 
2.4%
Other values (6) 90
8.0%

Hashtag_count
Real number (ℝ)

Distinct19
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.54902
Minimum10
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2025-09-13T14:16:45.142137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile11
Q117
median18
Q320
95-th percentile29
Maximum30
Range20
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.7981586
Coefficient of variation (CV)0.25867451
Kurtosis0.57442284
Mean18.54902
Median Absolute Deviation (MAD)1
Skewness0.59034408
Sum1892
Variance23.022326
MonotonicityNot monotonic
2025-09-13T14:16:45.248853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
17 22
21.6%
18 17
16.7%
19 16
15.7%
11 9
8.8%
22 5
 
4.9%
30 5
 
4.9%
20 4
 
3.9%
21 4
 
3.9%
13 3
 
2.9%
12 3
 
2.9%
Other values (9) 14
13.7%
ValueCountFrequency (%)
10 2
 
2.0%
11 9
8.8%
12 3
 
2.9%
13 3
 
2.9%
14 1
 
1.0%
16 1
 
1.0%
17 22
21.6%
18 17
16.7%
19 16
15.7%
20 4
 
3.9%
ValueCountFrequency (%)
30 5
4.9%
29 2
 
2.0%
28 2
 
2.0%
27 1
 
1.0%
25 1
 
1.0%
24 2
 
2.0%
23 2
 
2.0%
22 5
4.9%
21 4
3.9%
20 4
3.9%

Interactions

2025-09-13T14:16:32.614324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:40.778826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:44.107563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:46.811814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:49.494934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:51.867492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:54.919741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:57.573269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:00.243432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:03.354485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:05.818617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:08.043412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:10.719649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:13.593807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:15.980247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:18.873806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:21.267412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:23.852026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:27.153016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:29.888270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:32.733888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:41.064305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:44.202337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:46.939240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:49.604192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:51.985920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:55.051073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:57.760133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:00.395156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:03.459833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:05.944648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:08.157841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:10.949686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:13.676560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:16.072650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:19.013604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:21.396271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:23.951757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:27.289110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:30.029297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:32.851541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:41.309846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:44.294429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:47.060087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:49.729587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:52.116055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:55.176244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:57.908901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:00.511197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:03.569220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:06.142478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:08.372463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:11.104742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:13.806238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:16.232915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:19.116413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:21.508192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:24.083432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:27.455168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:30.172912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:32.961088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:41.430654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:44.428068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:47.193063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:49.876840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:52.247680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:55.321703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:58.011247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:00.659189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:03.703749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:06.281354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:08.524745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:11.212912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-09-13T14:15:41.550031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-09-13T14:16:00.839090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:03.850868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:06.403872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:08.641498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:11.328662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:14.086695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:16.646330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:19.337364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-09-13T14:16:24.376825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:27.723711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:30.543612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:33.224535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:41.689274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:44.685560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:47.411112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:50.113142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:52.472045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:55.594426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:58.359528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:00.972453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:03.984916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:06.513831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:08.776845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:11.445382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:14.263005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:16.793043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:19.439419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:21.819906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:24.557963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:27.826513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:30.666147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:33.335291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-09-13T14:15:51.335353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-09-13T14:15:56.871475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:59.729827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:02.372675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:05.206971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:07.613304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:10.006603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:13.135303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-09-13T14:16:20.783215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:23.275034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:26.594580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:29.153068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:32.135556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:34.753136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:43.686776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:46.409182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:49.062899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:51.455340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:54.145793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:56.991815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:59.916022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:02.528603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:05.432256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:07.711015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:10.129991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:13.248930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:15.652684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:18.361853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:20.906836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:23.455526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:26.704475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:29.363752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:32.245323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:34.882953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:43.837316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:46.527206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:49.187268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:51.561676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:54.598181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:57.144978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:00.043221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:02.628094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:05.567401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:07.813696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:10.285656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:13.349149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:15.777868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:18.502274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:21.023764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:23.583397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:26.834527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:29.536490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:32.380824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:35.002632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:43.967103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:46.700198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:49.355865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:51.684527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:54.735054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:15:57.355831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:00.136970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:03.185389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:05.683869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:07.928679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:10.485302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:13.487096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:15.877278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:18.677878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:21.148385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-09-13T14:16:26.992807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:29.687285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T14:16:32.510913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-09-13T14:16:45.457573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Caption_LengthCommentsEngagement_RateExplore_ratioFollowsFrom ExploreFrom HashtagsFrom HomeFrom OtherHashtag_countHashtag_ratioHome_ratioImpressionsLikesOther_ratioPost_IDProfile VisitsSavesSharesTop_sourcesaves_to_likes
Caption_Length1.0000.059-0.247-0.123-0.001-0.1180.115-0.243-0.1880.1400.257-0.1170.000-0.131-0.162-0.0280.106-0.229-0.3430.000-0.229
Comments0.0591.0000.192-0.087-0.0120.0420.2120.348-0.068-0.0270.202-0.0600.2790.357-0.155-0.2570.0980.2280.1530.2000.032
Engagement_Rate-0.2470.1921.0000.089-0.3510.024-0.2890.384-0.1570.017-0.2700.504-0.1730.241-0.050-0.259-0.4370.4870.3490.3020.588
Explore_ratio-0.123-0.0870.0891.0000.3070.930-0.0730.3270.115-0.207-0.452-0.1460.3480.269-0.0800.2830.1530.4700.3530.6740.570
Follows-0.001-0.012-0.3510.3071.0000.5050.5410.3640.610-0.4890.160-0.6740.7690.5710.2580.5730.7550.4400.2530.4650.217
From Explore-0.1180.0420.0240.9300.5051.0000.2010.4860.262-0.315-0.227-0.3700.6140.510-0.0310.3860.3410.6240.4590.6340.593
From Hashtags0.1150.212-0.289-0.0730.5410.2011.0000.1370.346-0.3090.813-0.8670.7710.6410.0290.3730.5790.3760.2620.5030.050
From Home-0.2430.3480.3840.3270.3640.4860.1371.0000.187-0.222-0.2320.0160.5550.712-0.0540.0270.2490.7410.6070.3600.593
From Other-0.188-0.068-0.1570.1150.6100.2620.3460.1871.000-0.4560.103-0.4700.4500.3720.8650.4440.6090.3010.2520.1680.149
Hashtag_count0.140-0.0270.017-0.207-0.489-0.315-0.309-0.222-0.4561.000-0.1010.407-0.446-0.425-0.264-0.356-0.424-0.415-0.0980.142-0.276
Hashtag_ratio0.2570.202-0.270-0.4520.160-0.2270.813-0.2320.103-0.1011.000-0.6340.3520.2470.0090.1210.291-0.030-0.0640.593-0.296
Home_ratio-0.117-0.0600.504-0.146-0.674-0.370-0.8670.016-0.4700.407-0.6341.000-0.784-0.508-0.138-0.536-0.660-0.284-0.1760.563-0.014
Impressions0.0000.279-0.1730.3480.7690.6140.7710.5550.450-0.4460.352-0.7841.0000.8550.0270.4710.6560.6820.4840.5510.374
Likes-0.1310.3570.2410.2690.5710.5100.6410.7120.372-0.4250.247-0.5080.8551.0000.0110.2790.4940.8490.5950.3670.504
Other_ratio-0.162-0.155-0.050-0.0800.258-0.0310.029-0.0540.865-0.2640.009-0.1380.0270.0111.0000.2010.3170.0010.0680.000-0.022
Post_ID-0.028-0.257-0.2590.2830.5730.3860.3730.0270.444-0.3560.121-0.5360.4710.2790.2011.0000.3740.3090.1460.5670.269
Profile Visits0.1060.098-0.4370.1530.7550.3410.5790.2490.609-0.4240.291-0.6600.6560.4940.3170.3741.0000.2570.1320.420-0.025
Saves-0.2290.2280.4870.4700.4400.6240.3760.7410.301-0.415-0.030-0.2840.6820.8490.0010.3090.2571.0000.6740.3860.866
Shares-0.3430.1530.3490.3530.2530.4590.2620.6070.252-0.098-0.064-0.1760.4840.5950.0680.1460.1320.6741.0000.3140.580
Top_source0.0000.2000.3020.6740.4650.6340.5030.3600.1680.1420.5930.5630.5510.3670.0000.5670.4200.3860.3141.0000.369
saves_to_likes-0.2290.0320.5880.5700.2170.5930.0500.5930.149-0.276-0.296-0.0140.3740.504-0.0220.269-0.0250.8660.5800.3691.000

Missing values

2025-09-13T14:16:35.251264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-13T14:16:35.559733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Post_IDImpressionsFrom HomeFrom HashtagsFrom ExploreFrom OtherSavesCommentsSharesLikesProfile VisitsFollowsCaptionHashtagsEngagement_RateHome_ratioExplore_ratioHashtag_ratioOther_ratiosaves_to_likesCaption_LengthTop_sourceHashtag_count
01392025861028619569895162352Here are some of the most important data visualizations that every Financial Data Analyst/Scientist should know.#finance #money #business #investing #investment #trading #stockmarket #data #datascience #dataanalysis #dataanalytics #datascientist #machinelearning #python #pythonprogramming #pythonprojects #pythoncode #artificialintelligence #ai #dataanalyst #amankharwal #thecleverprogrammer6.9897966.5969391.5790822.6224490.1428576.049383112Home_ratio22
125394272718381174781947142244810Here are some of the best data science project ideas on healthcare. If you want to become a data science professional in the healthcare domain then you must try to work on these projects.#healthcare #health #covid #data #datascience #dataanalysis #dataanalytics #datascientist #machinelearning #python #pythonprogramming #pythonprojects #pythoncode #artificialintelligence #ai #dataanalyst #amankharwal #thecleverprogrammer8.1386735.0556172.1764923.4074900.1446058.660714187Home_ratio18
234021208511880533411111316212Learn how to train a machine learning model and giving inputs to your trained model to make predictions using Python.#data #datascience #dataanalysis #dataanalytics #datascientist #machinelearning #python #pythonprogramming #pythonprojects #pythoncode #artificialintelligence #ai #deeplearning #machinelearningprojects #datascienceprojects #amankharwal #thecleverprogrammer #machinelearningmodels4.5759765.1852770.0000002.9544891.3255413.129771117Home_ratio18
344528270062193273172107213238Here’s how you can write a Python program to detect whether a sentence is a question or not. The idea here is to find the words that we see in the beginning of a question in the beginning of a sentence.#python #pythonprogramming #pythonprojects #pythoncode #pythonlearning #pythondeveloper #pythoncoding #pythonprogrammer #amankharwal #thecleverprogrammer #pythonprojects8.8780925.9628982.0583041.3714660.1612198.075117202Home_ratio11
452518170425527937965412380Plotting annotations while visualizing your data is considered good practice to make the graphs self-explanatory. Here is an example of how you can annotate a graph using Python.#datavisualization #datascience #data #dataanalytics #machinelearning #dataanalysis #artificialintelligence #python #datascientist #bigdata #deeplearning #dataviz #ai #analytics #technology #dataanalyst #programming #pythonprogramming #statistics #coding #businessintelligence #datamining #tech #business #computerscience #tableau #database #thecleverprogrammer #amankharwal9.0548056.7672761.1080221.0127080.1469427.804878178Home_ratio29
56388420461214329437471014492Here are some of the most important soft skills that every data scientist should have.#data #datascience #dataanalysis #dataanalytics #datascientist #machinelearning #python #pythonprogramming #pythonprojects #pythoncode #artificialintelligence #ai #deeplearning #algorithm #algorithms #machinelearningalgorithms #ml #amankharwal #thecleverprogrammer #softskills6.0504635.2677650.8470653.1256440.1107115.13888986Home_ratio20
672621154359933325225176260Learn how to analyze a candlestick chart as a data scientist or a financial analyst. I hope this resource will help you to invest and analyze stock markets.#stockmarket #investing #stocks #trading #money #investment #finance #forex #datavisualization #datascience #data #dataanalytics #machinelearning #dataanalysis #ai #candlestick #candlestickcharts3.9679515.8870661.2705072.2853870.0953832.894737156Home_ratio17
78354120716285006013549124126Here are some of the best books that you can follow to learn Python from scratch.#python #pythonprogramming #pythonprojects #pythoncode #pythonlearning #pythondeveloper #pythoncoding #pythonprogrammer #amankharwal #thecleverprogrammer #pythonprojects #pythonbooks #bookstagram7.6814465.8486301.4120301.7735100.16944410.88709781Home_ratio13
89374923848572484915568159364Here are some of the best data analysis project ideas that you should try and show on your resume. These projects will help you to show your data analysis skills.#dataanalytics #datascience #data #machinelearning #datavisualization #bigdata #artificialintelligence #datascientist #python #analytics #ai #dataanalysis #deeplearning #technology #programming #coding #dataanalyst #business #pythonprogramming #datamining #tech #businessintelligence #database #computerscience #statistics #powerbi #dataanalysisprojects #businessanalytics #thecleverprogrammer #amankharwal8.7490006.3590290.6615102.2859430.1307029.748428162Home_ratio30
9104115260911041784612263191316Here are two best ways to count the number of letters in a string using Python.#python #pythonprogramming #pythonprojects #pythoncode #pythonlearning #pythondeveloper #pythoncoding #pythonprogrammer #amankharwal #thecleverprogrammer #pythonprojects7.8250306.3402190.4325642.6828680.1117866.38743579Home_ratio11
Post_IDImpressionsFrom HomeFrom HashtagsFrom ExploreFrom OtherSavesCommentsSharesLikesProfile VisitsFollowsCaptionHashtagsEngagement_RateHome_ratioExplore_ratioHashtag_ratioOther_ratiosaves_to_likesCaption_LengthTop_sourceHashtag_count
109931771324492141123895615043233087096Here are some of the best resources to learn SQL for data science.#sql #mysql #datascience #datasciencejobs #datasciencetraining #datascienceeducation #datasciencecourse #data #dataanalysis #dataanalytics #datascientist #machinelearning #artificialintelligence #ai #deeplearning #machinelearningprojects #datascienceprojects #amankharwal #thecleverprogrammer4.7309891.3826006.9942981.2087170.31671716.36363666Explore_ratio19
1109455633813362113576149581632220Here are the best Python libraries for data visualization that you should learn for data science.#datavisualization #datascience #datasciencejobs #datasciencetraining #datascienceeducation #datasciencecourse #data #dataanalysis #dataanalytics #datascientist #machinelearning #artificialintelligence #ai #deeplearning #machinelearningprojects #datascienceprojects #amankharwal #thecleverprogrammer5.8421716.8542152.0402660.6507280.1366179.14110497Home_ratio18
111954842165869420363105564864630Learn how to create an interactive language translator using the Python programming language.#python #pythonprogramming #pythoncode #pythonlearning #pythondeveloper #pythonprogrammer #pythonprojects #python3 #pythoncoding #pythonprogramminglanguage #amankharwal #thecleverprogrammer #nlp #naturallanguageprocessing3.1185463.4242054.2048741.4332920.6402316.39534993Explore_ratio14
112961114944397475762532734132106158Python is one of the best programming languages for numerical calculations. So you should know how to calculate mean, median and mode using Python without using any built-in Python library or module. Here’s how to calculate mean, median, and mode using Python.#python #pythonprogramming #pythoncode #pythonlearning #pythondeveloper #pythonprogrammer #pythonprojects #python3 #pythoncoding #pythonprogramminglanguage #amankharwal #thecleverprogrammer4.4847073.9815235.1681770.6700150.04753813.000000260Explore_ratio12
11397102062371162460001171821017172237100Practice these 90+ Data Science Projects For Beginners Solved & Explained using Python. Find all these projects from the link in bio.#datascience #datasciencejobs #datasciencetraining #datascienceeducation #datasciencecourse #data #dataanalysis #dataanalytics #datascientist #machinelearning #artificialintelligence #ai #deeplearning #machinelearningprojects #datascienceprojects #amankharwal #thecleverprogrammer3.7330982.3231435.8788951.5912210.11463810.581395133Explore_ratio17
1149813700518530415352775732383737380Here are some of the best data science certifications that you can choose from in 2022.#datascience #datasciencejobs #datasciencetraining #datascienceeducation #datasciencecourse #data #dataanalysis #dataanalytics #datascientist #machinelearning #artificialintelligence #ai #deeplearning #machinelearningprojects #datascienceprojects #amankharwal #thecleverprogrammer7.1970803.7846723.9065692.2197080.05620415.36193087Explore_ratio17
11599573119231368226665135411482018Clustering is a machine learning technique used to classify data points, charaterized by some specific features into groups. It is an unsupervised machine learning method where the data we deal with is not labelled. Here are some of the best Machine Learning project ideas on Clustering that you should try.#machinelearning #machinelearningalgorithms #datascience #dataanalysis #dataanalytics #datascientist #python #pythonprogramming #pythonprojects #pythoncode #artificialintelligence #ai #deeplearning #algorithm #algorithms #amankharwal #thecleverprogrammer #clustering5.0253013.3554353.9539352.3870180.1134189.121622307Explore_ratio18
1161004139113315381367333601923410Clustering music genres is a task of grouping music based on the similarities in their audio characteristics. Here you will learn how to do clustering analysis of music genres with Machine Learning using Python.#machinelearning #machinelearningalgorithms #datascience #dataanalysis #dataanalytics #datascientist #python #pythonprogramming #pythonprojects #pythoncode #artificialintelligence #ai #deeplearning #algorithm #algorithms #amankharwal #thecleverprogrammer #clustering3.1166952.7373763.3027303.7158730.0797293.913043211Hashtag_ratio18
11710132695118153147174141701095275549148214Here are some of the best data science certifications that you can choose from in 2022.#datascience #datasciencejobs #datasciencetraining #datascienceeducation #datasciencecourse #data #dataanalysis #dataanalytics #datascientist #machinelearning #artificialintelligence #ai #deeplearning #machinelearningprojects #datascienceprojects #amankharwal #thecleverprogrammer5.2638023.6137025.3261970.9625320.05199619.94535587Explore_ratio17
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